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What should a sim-to-real evaluation measure?
There are two different questions behind a claim that simulation is useful for robotics. First, does a policy transferred from simulation perform acceptably on hardware? Second, do simulation results predict real-world performance across policies or conditions? The 2026 Annual Review survey, The Reality Gap in Robotics: Challenges, Solutions, and Best Practices, treats reality-gap measures and transfer-performance measures as distinct categories.
Keep those questions separate in your results. A policy may perform well on the robot even if simulation did not predict its performance, or simulation may rank several policies correctly even though none performs well enough in reality.
How to measure performance on the real robot
Report task success over repeated trials
Define the success condition before testing, then report the proportion of real-robot trials that meet it. State the number of trials and how the starting states and other test conditions were selected. A single successful rollout shows that a policy can succeed once; it does not establish how reliably it will do so.
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Add a task-specific measure
Success or failure alone can hide how close a run came to completion, how efficiently it progressed, or how it failed. Pair the success rate with a metric suited to the task, such as time to goal or path efficiency for navigation, or object distance to its target for manipulation. For reinforcement-learning studies, cumulative reward can provide a finer-grained measure, but only when the reward definition is consistent and interpretable across simulation and hardware.
Do not treat metrics from different tasks or differently defined rewards as if they shared a scale. State what each measure means and how it is calculated.
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Record failure types and safety outcomes
Break down important failure modes rather than reporting only an average success rate. Two policies with the same success rate may differ in robustness or in the severity of their failures. Include safety-relevant outcomes explicitly so that a reader can distinguish an unsuccessful but harmless attempt from a consequential failure.
How to test whether simulation predicts reality
Predictive validity requires comparisons across multiple policies or method-task conditions, evaluated in both simulation and reality. Compare the paired scores and report a correlation measure, preferably with the per-policy results or a scatter plot so readers can inspect outliers and absolute performance.
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The Annual Review survey describes the sim-to-real correlation coefficient (SRCC) using Pearson correlation between simulated and real task performance. Pearson correlation indicates how closely scores move together; it does not, by itself, show that simulated scores match real scores in magnitude. A strong correlation can coexist with poor real-world performance across all the tested policies. If ranking policies is the central question, a rank-correlation measure such as Spearman’s rho can also be informative. Label the statistic precisely rather than treating different correlation measures as interchangeable.
Published results illustrate why the measure and setup matter:
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- In a 2025 study, Xuanlin Li and colleagues reported more than 1,500 paired simulation-and-real evaluations in SIMPLER, spanning two embodiments and eight manipulation task families. They reported strong correlation between simulated and real performance in those evaluated settings. This is evidence about that benchmark and task domain, not a recommended sample size or a guarantee for other robots and tasks.
- The H2RBench project page, marked CoRL 2026, reports Pearson r = 0.89, Spearman rho = 0.85, and MMRV = 0.06 across method-task configurations for its human-to-robot transfer benchmark. These are benchmark-specific reported results; the figures should not be read as expected values for unrelated studies.
- Kadian and colleagues reported an SRCC of 0.18 for Habitat success, which rose to 0.844 after simulator parameter tuning in their 2020 study. The contrast shows that predictive validity can depend on simulator configuration in a particular study; neither value is a general target or baseline.
How to design a useful paired evaluation
- Define the task and metrics. Specify the pass/fail success condition and any continuous task measures before running trials. For reward-based work, document the reward definition and ensure the simulation and hardware scores are meaningfully comparable.
- Align the paired conditions. Evaluate the same policy versions in simulation and on the real robot. Document the embodiment, hardware, sensors, control interface, task setup, scenes, and objects. If a condition cannot be matched, state the difference.
- Repeat across relevant starts and shifts. Test more than one initial state and vary conditions that matter for deployment. Report the trial protocol, trial count, and distribution shifts tested; do not present one rollout as evidence of robustness.
- Separate transfer from prediction. Use real-robot task scores to describe transfer performance. To claim that simulation predicts outcomes, compare multiple policies or conditions across both domains and report their paired results and correlation.
- Inspect mismatches and exceptions. Describe relevant visual and control differences between simulated and real setups, any calibration or mitigation, and cases where the simulated result did not match reality. A convincing-looking scene or high-fidelity simulator is not, on its own, evidence of predictive validity.
- Scope the conclusion. Tie claims to the tested robot, task family, interfaces, and conditions. Report safety-relevant outcomes and failure modes along with aggregate metrics.
The 2026 Annual Review survey argues that exact replication of real dynamics and observations is not required for transfer; its framing is that robust performance despite differences is the relevant objective. Treat that as the review’s perspective, not a universal recipe that removes the need to describe or test those differences.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What SIMPLER and H2RBench can—and cannot—show
These benchmarks address different evaluation needs. SIMPLER offers simulation-based evaluation for common real-robot manipulation setups and reports paired simulation-real results in its evaluated settings. H2RBench establishes a shared Real2Sim protocol for human-to-robot transfer using reconstructed real-world scenes. Neither is established as a universal benchmark for every robot or task family.
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| Benchmark | Purpose and scope | Reported evaluation evidence | What to check before using it |
|---|---|---|---|
| SIMPLER | Simulation-based evaluation for common real-robot manipulation setups; the 2025 paper covers two embodiments and eight task families. | More than 1,500 paired simulation-and-real evaluations, with strong reported correlation in the evaluated manipulation settings (Li et al., PMLR, 2025). | Whether its manipulation tasks, robot embodiment, observation and action interfaces, and tested distribution shifts match the study’s intended use. |
| H2RBench | A shared Real2Sim protocol for human-to-robot transfer across four manipulation tasks reconstructed from real-world scenes. | Its CoRL 2026 project page reports Pearson r = 0.89, Spearman rho = 0.85, and MMRV = 0.06 across method-task configurations. | Whether the human-to-robot transfer protocol, real-world supervision conditions, scenes, embodiment, and interfaces match the comparison being made. |
For reproducibility, align the embodiment, tasks, scenes, objects, and real-world supervision conditions where possible, and disclose deviations. H2RBench was designed around a shared protocol because earlier human-to-robot transfer evaluations differed across these dimensions. A result from either benchmark should not be extended to navigation or locomotion without evidence from those domains.
How much testing is enough?
The reviewed sources do not establish a universal minimum trial count, confidence-interval method, or pass threshold that applies across manipulation, navigation, and locomotion. Choose a trial design suited to the task and intended claim, report it clearly, and avoid presenting a particular count or cutoff as a general standard.
In 2021, Bhairav Mehta, Ankur Handa, Dieter Fox, and Fabio Ramos observed in A User’s Guide to Calibrating Robotic Simulators that analysis of sim-to-real methods was often conducted “in an ad-hoc manner without a consistent set of tests and metrics for comparison.” A clearly defined task, paired conditions, repeated trials, transparent metrics, and explicit limits make results easier to interpret and reproduce.
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